Training a machine to automate spot pricing of logistics services in a large-scale network

ABSTRACT

A machine learning algorithm is trained to learn to cluster a plurality of original-destination routes in a network for transporting cargo into a plurality of clusters based on similarities of the original-destination routes, and to learn to cluster the plurality of clusters into a plurality of subgroups based on customer behavior. Influencing criteria associated with each of the subgroups may be determined and based on the influencing criteria, a price elasticity curve for each of the subgroups may be generated. Based on the price elasticity curve and current network traffic, cargo transportation price associated with each of the subgroups may be determined.

BACKGROUND

The present application relates generally to computers and computerapplications, and more particularly to cargo logistics and loadbalancing based on data and machine learning.

Pricing of air cargo depends on a mix of long term contracts and spotmarket pricing. At present the spot pricing is performed manually orbased on standard rate sheet. Each pricing request may include pricingfor several routes with large number of variability. The combination ofrouting network and large number of variables for each spot pricingrequest renders the problem very complex. At present there is a lack ofoff-the-shelf system tools for spot pricing at the necessary scale.

BRIEF SUMMARY

A method and system of training a machine to automate spot pricing oflogistics services may be provided. The method, in one aspect, mayinclude receiving a plurality of original-destination routes in anetwork for transporting cargo. The method may also include clusteringthe plurality of original-destination routes into a plurality ofclusters based on similarities of the original-destination routes. Themethod may further include clustering the plurality of clusters into aplurality of subgroups based on customer behavior, wherein a machinelearning algorithm is trained to learn to perform the clustering and thesteps of clustering are performed by executing the machine learningalgorithm. The method may also include determining influencing criteriaassociated with each of the subgroups. The method may further include,based on the influencing criteria, generating a price elasticity curvefor each of the subgroups. The method may also include, based on theprice elasticity curve and current network traffic, determining cargotransportation price associated with each of the subgroups.

A system of training a machine to automate spot pricing of logisticsservices, in one aspect, may include at least one hardware processor. Adisplay device may be coupled to the hardware processor. The hardwareprocessor may be operable to receive a plurality of original-destinationroutes in a network for transporting cargo. The hardware processor maybe further operable to learn to train itself to cluster the plurality oforiginal-destination routes into a plurality of clusters based onsimilarities of the original-destination routes. The hardware processormay be further operable to learn to train itself to cluster theplurality of clusters into a plurality of subgroups based on customerbehavior. The hardware processor may be further operable to determineinfluencing criteria associated with each of the subgroups, and based onthe influencing criteria, generate a price elasticity curve for each ofthe subgroups. The hardware processor may be further operable to, basedon the price elasticity curve and current network traffic, determinecargo transportation price associated with each of the subgroups. Thehardware processor may be further operable to visually display thesubgroups in a graphical user interface on the display device.

A computer readable storage medium storing a program of instructionsexecutable by a machine to perform one or more methods described hereinalso may be provided.

Further features as well as the structure and operation of variousembodiments are described in detail below with reference to theaccompanying drawings. In the drawings, like reference numbers indicateidentical or functionally similar elements.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram showing system architecture in one embodiment of thepresent disclosure.

FIG. 2 shows a visual graphics example of co-clustered data in oneembodiment of the present disclosure.

FIGS. 3A and 3B show examples of load adjusted price optimization in oneembodiment of the present disclosure.

FIG. 4 shows different levels of quote classification in one embodimentof the present disclosure in determining spot pricing.

FIG. 5 illustrates an example of multiple routes that may be consideredin cargo pricing in one embodiment of the present disclosure.

FIG. 6 is a flow diagram showing a method in one embodiment of thepresent disclosure.

FIG. 7 illustrates a schematic of an example computer or processingsystem that may implement a cargo logistics service system in oneembodiment of the present disclosure.

DETAILED DESCRIPTION

A system, method and computer program product that implements anautomated pricing engine for spot pricing, for example, including liveprice quotes for all the routers of each origin-destination (O-D) pairin a global logistics network may be provided. In one aspect, pricesolutions may be contingent on the traffic and material flows in thenetwork.

In one aspect, a network-based O-D clustering and quote segmentationalgorithms are generated to solve the complexity and scale of the spotpricing. In another aspect, demand shaping is performed through thenetwork in that price solutions are contingent on the traffic andmaterial flows in the network. The system and method may handle theservice complexity with the scale of global logistics network.

An automated solution may be provided for pricing spots quotes in alogistics market. For example, cargo product specialties includecomplexities such as weight, density, special handling requirement, andflexibility of capacity utilization. Existing solutions, e.g., serverconfiguration pricing, do not handle the large scale logistics network,for example, in the presence of many routes where they have unbalancedtraffic and where little historical data is available. Multi-routepricing may price multiple routes based on service requests and trafficcongestions. Marginal opportunity cost may be estimated based on networkutilization and flow balance. For example, FIG. 5 shows a simple exampleof multiple routes considered in cargo pricing in one embodiment of thepresent disclosure. A carrier may view optimal price by route. Lowprices may be available for certain arrival date, while large premiummay be added on next-day arrival. Route-based pricing may also lead topremiums or discounts on specific hubs, freighter capacity, specialhandling, fresh product, and/or other factors.

Automated pricing for air cargo service network has technicalchallenges, for example, for the following reasons. Air cargo pricing isdifficult to automate because of simultaneous challenges of scale andcomplexity. For example, 30,000 plus OD requires robust automation ofcalibration and model selection steps. In contrast to passenger travel,each quote of cargo contains a unique combination of weight, volume,shape, density, and other factors, and involves unique competitivedynamics in each market based on routing and capacity of differentcarriers. Another challenge is jointly pricing a variety of routes withuncertain status of demand-capacity balance through the network. In mostcases, very little historical data is available for a specificcombination of market, customer, and quote type.

FIG. 1 is a diagram showing system architecture in one embodiment of thepresent disclosure. Analytics components 126 may execute on one or morehardware processors. Automatic clustering 102 may include ODco-clustering and quote clustering. Many ODs have too few quotes to beable to derive a price elasticity curve. OD co-clustering in oneembodiment of the present disclosure may include grouping similar ODsinto one cluster. Such clustering also improves runtime performance of acomputer system. ODs and OD clusters may contain large range of quoterequests that prevent a good fit for a single price elasticity curve.Quotes may be differentiated by weight, volume, urgency, number ofpieces, and a variety of special handling requirements, along withseasonal, market, and customer characteristics. Quote clustering in oneembodiment of the present disclosure may include isolating subgroups ofquotes with homogenous customer behavior. For example, customers withina cluster have a similar price threshold, and value a similar set ofquote features. The subgroups are referred to as “quote clusters”.

Automatic clustering 102 may receive as input OD data such as differentroutes, historical quotes associated with transporting cargo via thoseroutes.

In a hierarchical logistic regression model (HLRM), the automaticclustering 102 may include fitting quote data to observed winprobability, while addressing dependency in clustered data (e.g.,competitiveness of OD pairs). The OD effect μ on win probabilityprovides a scientific effect on win/loss that is computed from the data.The OD effect μ is a specific effect that shifts the win probabilitycurve either up or down.

Logit {E(y _(ijk))}=X _(ijk)β_(ij) +Z _(ijk) u _(ij)+ϵ_(ijk)

-   -   (1) y_(ijk): win or loss for quote k,    -   (2) β_(ij): common effects on the cargo attributes, including        for example, Chargeable Weight (CWT), Volume (VOL).    -   (3) u_(ij): OD pair effect for original (ORIG)=i and destination        (DEST)=j.        E represents “Expectation”, a standard term in probability.        X is vector of attributes for quote k. Z has a value of 0 or 1        represents an indicator mapping k to the OD, represented by the        pair (i,j).        ϵ is a random noise term.        This step initially fits all quotes using a single model, where        μ captures deviation of OD from that universal model. The next        step clusters OD for similar μ.

OD co-clustering in one embodiment evaluates permutations of origin anddestination in order to draw a correspondence structure (e.g., patternrecognition) for the most similar effects, e.g., a specific effect thatshifts the win probability curve. Automatic clustering 102 co-clustersthe OD pairs based on the market effect μ. FIG. 2 shows a visualgraphics example of co-clustered data. Such graphics may be rendered ona graphical user interface, allowing users to be able to view theclusters, for example, displayed on a dashboard.

Quote clustering may include merging a decision tree learning (CART)algorithm with custom logic to create interplay between segmentation andregression.

Price influence ranking and selection 104 may include variableselection. Variable selection finds relevant price influencers for eachquote cluster without over-fitting by automatically filtering mostimportant price influencers from a pool of influencers 116 via LASSOtechnique and cross-validation. LASSO is an established method that willeliminate variables that are not useful predictors in the model. Thistechnique fits a logistic regression model with an extra penalty placedon the size of each parameter so parameters will minimize penalty byshrinking to zero when not important from prediction. Another method todetermine the most important price influencers is to remove variablesthat do not show impact statistically through their p-value in theregression. In one embodiment, the methodology of the present disclosuremay perform both of these methods at each node of the CART tree and thenevaluate each node according to rule-based checks that ensure the modelis within a range of user expectations. The methodology of the presentdisclosure in one embodiment uses this rule selection to select the bestnodes to include in the clustering. These rule requirements may functionas a third criterion for variable selection (determining the mostimportant price influencers).

Examples of price influencers may include, but are not limited to, CWT,leadtime, number of pieces, number of stops, historical win rates, typeof consumers, type of products, type of freights, competition,seasonality, and week-end effects. Price influencers may be correlated,e.g., CWT and number of pieces, freighter and week-end. L-1 penalizedmethod (LASSO) may be executed to reduce correlation, and a p-valuethreshold may be executed to filter out statistically insignificantprice influencers.

The pool of influencers 116 may be retrieved from a database or storagedevice, and may include price influencer refined for market or segment.For example, a price influencer scanning and detection component 118executing on a hardware processor may determine from a historicaldatabase 120 the price influencers to add or remove, and generate theprice influencer pool 116. The price influencer scanning and detectioncomponent 118 may extract all potential influencers from available datato use a starting point for automated selection. The historical database120 may store quote data 122 such as historical quote pricing andwin/loss associated with the historical quote pricing, quotecharacteristics and special handling requirements, and external data 124such as competitive pricing, market capacity and seasonal factors. Theprice influencer pool 116 may include items such as: Rate; CWT; Density;Weight; Volume; Leadtime, i.e., (LAT—date created); number of Pieces;Freighter/Belly Route; number of Stops; Customer Industry; CustomerSize; Customer Relationship Type; Customer Wallet Share; DOW; ProductCode (Fresh, Express, etc.); Special Handling Flags (Large, Heavy,etc.); Seasonality Index (score of CWT demand relative to average);Seasonality Index (score of revenue demand relative to average);Seasonality Index (score of number of orders demand relative toaverage); Historical Customer W/L; Historical Customer Pricing inmarket; Historical Customer Pricing overall; Shipper Capacity Shareagainst Market; Average price quoted to customer in market; Averageprice quoted to customer overall; Flexibility (LAT—ready on date);Customer percent (%) Revenue through spot market (OD); Customer % CWTthrough spot market (OD); Customer % Revenue through spot market(overall); Customer % CWT through spot market (overall). From a numberof such potential influencers, the price influencer ranking andselection component 104 may sort through and find the influencers thatmatter in each market. Machine learning algorithm such as LASSOregularization of a logistic regression implemented on a machine buildsa model that determines the influencers, and another learning algorithmsuch as cross-validation calibrates the model.

Price elasticity computation 106 may include a win probability modelthat fits appropriate price elasticity curve for each subgroup of quotesin each route by estimating likelihood to win for multiple routes. Acurve is fit according to the Multinomial Logit choice model with quoteoutcome as the response variable and rate as a parameter along with eachof the price influencers selected in 104. This takes the formP_(ijck)({circumflex over (β)}_(ijc))=e^({circumflex over (X)}) ^(ijck)^({circumflex over (β)}) ^(ijc) /(1+e^({circumflex over (X)}) ^(ijck)^({circumflex over (β)}) ^(ijc) ), where P_(ijck) is the predicted winprobability for quote k in segment c of the origin-destination pair ij,{circumflex over (X)}_(ijck) and {circumflex over (β)}_(ijc) containsquote characteristics and model coefficients for quote k, restricted tothe set of price influencers selected previously for segment c.

Price optimization 108 finds prices of all possible routes that maximizeexpected revenue contingent on network traffics by generating algorithmsto find maximum value for revenue win probability with penalty for heavytraffic. After fitting the coefficients in 106, the win probabilitycurve P_(ijck)({circumflex over (β)}_(ijc)) is rewritten asP_(ijck)(r_(k)) to depend only on the rate r_(k). Optimization at 108chooses r_(k) to maximize a function r_(k)P_(ijck)(r_(k)). This gives arevenue-maximizing price, which may then be adjusted up or down toaccount for over or under-utilization of the route being priced. In oneaspect, the price optimization 108 may be triggered based on receiving aquote request 112. Receiving a quote request 112 may trigger executionof a quote dispatcher 114 on a hardware processor, which may invoke theprice optimization 108.

FIGS. 3A and 3B show examples of load-adjusted price optimization in oneembodiment of the present disclosure. In FIG. 3A, the rightmost verticalbar passes through the revenue curve at the optimal point, and the lowerhorizontal bar indicates the optimal win probability is 70% withadjustments for load. In FIG. 3B, a load curve indicates % of capacityreserved, with days until departure on the x-axis. If load falls too lowat any point, the load can be increased by a shift of win probability toa level above the unadjusted optimal. The leftmost and upper bars onFIG. 3A indicate the rate and win probability associated with such ashift. Here the win probability jumps to 85% at a new, lower price. Anexample use case may include utilization and load curve includingdefinition of ideal and historical load curve, definition of feasiblecorridor, adjustment of win probability (price) whenever corridor isviolated. An adjustment may be made via a business rule. Business rules,for example, may be stored in a database of special rules (e.g., FIG. 1,110). An example of a business rule may include, “−10% load->(implies)85% win probability”. For instance, the provider may attempt to balanceload by adjusting targeted win probability. For example, if the load is10% below target level, the provider offers low prices that correspondto a high, 85% win probability until the load is raised to the desiredlevel. An adjustment may be made via calculation. An example of acalculation may include “reach 85% load at departure (DEP)”. Forexample, the method in one embodiment of the present disclosurecalculates its own target win probabilities in order to achieve thedesired 85% load at departure. This is based on a predicted pattern offuture arrivals from the current time up until departure.

In one embodiment, the variable selection at 104, price elasticityestimation at 106 and price optimization at 108 may be performediteratively, with the rules for segmenting quotes updated before eachiteration. During each iteration, an alternative segmentation rule istested and evaluated based on the outcomes of the variable selection,price elasticity and price optimization. The alternative rule isimplemented if it results in improvement in the result, for example,producing reasonable optimal prices, better prediction and betterrevenue. The procedure stops when no improvements are possible withinthe context of a predefined set of allowable segmentation rules. FIG. 4shows different levels of iteration in one embodiment of the presentdisclosure in determining spot pricing. In the case of FIG. 4, alternatesegmentations may be created by adding or collapsing nodes of a decisiontree. The solution shown in FIG. 4 contains segmentation rules based onthe density, weight (AWT), and volume (VOL) of a quote.

Referring back to FIG. 1, the output 128 of the processing of theanalytics components may include optimal pricing, price influencerbreakdown, what-if calculator, and quote segment dashboard. Optimalpricing entails a display of the optimal rate to charge for eachfeasible routing of the quote. Price influencer breakdown providesdiagnostic output to the pricing agent that explains the influencerswith the biggest impact on price for this quote. What-if calculatorallows the pricing agent to create alternate quotes by varying certainelements and observe the resulting change in optimal rate. Quote segmentdashboard provides information on the segment of quotes within which thequote was placed and how the quote compares to the segment as a wholewith respect to key price influencers.

The system of the present disclosure in one embodiment provides for ajoint price optimization for multiple routes contingent on the networktraffic; clustering OD routes based on the global marketcompetitiveness; a hierarchical segmentation considering selected cargofeatures; a quote win probability model responding to automaticallyselected price influencers such as physical dimension, special handlingrequirement, and/or others; and an automatically adaptive feedbacksystem to integrate segmentation and pricing models. In one embodiment,the system may price each route based on the network traffic to achieveglobal pricing optimization balancing demand and capacity through thenetwork; enable real-time differentiated pricing strategy for a varietyof cargo services for each local market, incorporating unique inputssuch as regional competition mapping, customer's wallet shares,seasonality, routes, and/or other criteria, to provide better globalmarket splits.

In one aspect, the price automation of the present disclosure may bebased on statistical modeling and price optimization. Quote complexitymay include price differentiation in physical dimensions and other cargoproperties (e.g., special handling and date flexibility). Global scalingmay be enabled via automated calibration and model selection for large(e.g., 30,000) OD network and multiple routes for each OD.

Product segmentation may include quotes segmented by cargo physics andclient relationship. An iterative algorithm balances complexity and dataavailability in partitioning complex product space. OD market clusteringintegrates two methods to segment OD pairs, and for example,characterizes market competitiveness. Demand shaping through network mayinclude pricing multiple routes to maximize the expected revenue withthe penalty for heavy traffic.

The method of the present disclosure in one embodiment, for example, inproviding a logistics service, may simultaneously determine optimalprices of multiple shipping routes for a request-for-quote (RFQ) oflogistic services. In one embodiment, a hierarchical co-clusteringmethod is applied to cluster a large number (e.g., thousands) of ODpairs in the network, which considers both geographic and marketconditions. In each cluster, a learning algorithm is used to furthersegment cargo services based on their attributes and servicerequirement. For example, the processes at 104, 106, 108 are iterativelyperformed for improving the result, and for example, as shown in FIG. 4.In one embodiment, a service requirement is a factor considered in theclustering and pricing module. For example, pricing solutions may beprovided for all the proposed routes of logistic services. In oneaspect, the method may calibrate the impact of inventory status andcapacity usage on the pricing decisions through the network. Accordingto a method in one embodiment, a carrier such as an airline company whocontrols a network may be able to set the prices for all routes in thenetwork. The method in one embodiment models price elasticity andoptimizes spot prices at a global scale. The method may also modelcustomer utility for each possible route so that a utility-maximizingand/or carrier profit-maximizing choice may be made for each quote. Inone embodiment, a demand model may be learned from actual data.

FIG. 6 is a flow diagram showing a method in one embodiment of thepresent disclosure. At 602, a plurality of original-destination routesin a network for transporting cargo may be received. At 604, theplurality of original-destination routes are clustered into a pluralityof clusters based on similarities of the original-destination routes. At606, the plurality of clusters are clustered or grouped into a pluralityof subgroups based on customer behavior. In one embodiment, a machinelearning algorithm is trained to learn to perform the clustering and thesteps of clustering are performed by executing the machine learningalgorithm. An example is a co-clustering algorithm described withreference to FIG. 1 at 102.

At 608, price influencers, e.g., also referred to as influencingcriteria, associated with each of the subgroups are determined, forexample, as described above with reference to FIG. 1 at 104. In oneembodiment, this may be done using LASSO regularization of a logisticregression model and model parameters are calibrated usingcross-validation.

At 610, based on the influencing criteria, a price elasticity curve foreach of the subgroups is generated, for example, as described above withreference to FIG. 1 at 106. In one embodiment, the curve is estimatedusing maximum likelihood estimation to fit a multinomial logit choicemodel.

At 612, based on the price elasticity curve and current network traffic,cargo transportation price associated with each of the subgroups may bedetermined. For example, as described above, an optimization functionmay be executed that maximizes revenue and win probability of a pricequote with a penalty for heavy traffic.

In one aspect, the method may generate a confidence score during theprice elasticity estimation step, and this may also depend on the priceoptimization results. The elasticity estimation procedure producesmeasures of statistical confidence, e.g., the predicted accuracy of themodel and the p-value associated with the rate term in the regressionthat fits the win probability curve. This is combined with rules-basedchecks on the suggested optimal prices and projected revenue gains toassign an overall confidence score to the model.

The method and system in one embodiment of the present disclosure mayimprove on existing computer technology for cargo pricing by addingautomation that enables the computer to recommend optimal prices for allroutes in a network. This improves on existing systems which can capturepricing data but does not generate and provide optimal prices. In somecases, the technology of the present disclosure may be used as part of asystem of devices that tracks the current state of the logistic network.Tracking data may include the location of vehicles and/or specificloads. The method and system in one embodiment improves on existingtracking technology by continually observing load in the network anddetermining optimal prices for requested routes in real time based onnetwork state. In this way, the system goes beyond tracking to includecapabilities for dynamically balancing load in the network throughoptimal spot pricing. The method and system may thus include dynamicallybalancing load in a logistic network transporting cargo.

FIG. 7 illustrates a schematic of an example computer or processingsystem that may implement a cargo logistics service system in oneembodiment of the present disclosure. The computer system is only oneexample of a suitable processing system and is not intended to suggestany limitation as to the scope of use or functionality of embodiments ofthe methodology described herein. The processing system shown may beoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with the processing system shown in FIG. 7 may include,but are not limited to, personal computer systems, server computersystems, thin clients, thick clients, handheld or laptop devices,multiprocessor systems, microprocessor-based systems, set top boxes,programmable consumer electronics, network PCs, minicomputer systems,mainframe computer systems, and distributed cloud computing environmentsthat include any of the above systems or devices, and the like.

The computer system may be described in the general context of computersystem executable instructions, such as program modules, being executedby a computer system. Generally, program modules may include routines,programs, objects, components, logic, data structures, and so on thatperform particular tasks or implement particular abstract data types.The computer system may be practiced in distributed cloud computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. In a distributed cloudcomputing environment, program modules may be located in both local andremote computer system storage media including memory storage devices.

The components of computer system may include, but are not limited to,one or more processors or processing units 12, a system memory 16, and abus 14 that couples various system components including system memory 16to processor 12. The processor 12 may include a module 30 that performsthe methods described herein. The module 30 may be programmed into theintegrated circuits of the processor 12, or loaded from memory 16,storage device 18, or network 24 or combinations thereof.

Bus 14 may represent one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. By way of example, and notlimitation, such architectures include Industry Standard Architecture(ISA) bus, Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA)bus, Video Electronics Standards Association (VESA) local bus, andPeripheral Component Interconnects (PCI) bus.

Computer system may include a variety of computer system readable media.Such media may be any available media that is accessible by computersystem, and it may include both volatile and non-volatile media,removable and non-removable media.

System memory 16 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) and/or cachememory or others. Computer system may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 18 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(e.g., a “hard drive”). Although not shown, a magnetic disk drive forreading from and writing to a removable, non-volatile magnetic disk(e.g., a “floppy disk”), and an optical disk drive for reading from orwriting to a removable, non-volatile optical disk such as a CD-ROM,DVD-ROM or other optical media can be provided. In such instances, eachcan be connected to bus 14 by one or more data media interfaces.

Computer system may also communicate with one or more external devices26 such as a keyboard, a pointing device, a display 28, etc.; one ormore devices that enable a user to interact with computer system; and/orany devices (e.g., network card, modem, etc.) that enable computersystem to communicate with one or more other computing devices. Suchcommunication can occur via Input/Output (I/O) interfaces 20.

Still yet, computer system can communicate with one or more networks 24such as a local area network (LAN), a general wide area network (WAN),and/or a public network (e.g., the Internet) via network adapter 22. Asdepicted, network adapter 22 communicates with the other components ofcomputer system via bus 14. It should be understood that although notshown, other hardware and/or software components could be used inconjunction with computer system. Examples include, but are not limitedto: microcode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements, if any, in the claims below areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description of the present invention has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the invention in the form disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the invention.The embodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

1. A method of training a machine to automate spot pricing of logisticsservices, the method performed by at least one hardware processor,comprising: receiving a plurality of original-destination routes in anetwork for transporting cargo; clustering the plurality oforiginal-destination routes into a plurality of clusters based onsimilarities of the original-destination routes; clustering theplurality of clusters into a plurality of subgroups based on customerrelated factors, wherein based on a machine learning algorithm, amachine learning model is trained to perform the clustering and theclustering is performed by the trained machine learning model;determining influencing factors associated with each of the subgroups;based on the influencing factors, generating a price elasticity curvefor each of the subgroups; based on the price elasticity curve andcurrent network traffic, determining cargo transportation priceassociated with each of the subgroups, wherein the influencing factorscomprise at least rate, chargeable weight (CWT), density, weight,volume, leadtime, number of pieces, freighter route, number of stops,customer industry, product code, special handling flag, seasonalityindices indicating CWT demand relative to average CWT, revenue demandrelative to average revenue and orders demand relative to averageorders, shipper capacity, average price quoted to customer in market,average price quoted to customer overall, customer percent (%) revenuethrough spot market, and customer % CWT through spot market; anddynamically balancing load in air traffic network in real-time viaproviding the cargo transportation price.
 2. The method of claim 1,wherein the cargo transportation price is determined for each of thesubgroups simultaneously.
 3. (canceled)
 4. The method of claim 1,wherein the influencing factors are correlated.
 5. The method of claim1, wherein the determining influencing factors comprises executing aLASSO technique.
 6. The method of claim 1, wherein the determining cargotransportation price comprises executing an optimization function thatmaximizes revenue and a probability that a quoted price will beaccepted.
 7. The method of claim 1, further comprising visuallydisplaying the subgroups in a graphical user interface.
 8. The method ofclaim 1, further comprising iterating the determining of the influencingfactors, generating of the price elasticity curve, and the determiningof the cargo transportation price.
 9. A computer readable storage mediumstoring a program of instructions executable by a machine to perform amethod of training a machine to automate spot pricing of logisticsservices, the method comprising: receiving a plurality oforiginal-destination routes in a network for transporting cargo;clustering the plurality of original-destination routes into a pluralityof clusters based on similarities of the original-destination routes;clustering the plurality of clusters into a plurality of subgroups basedon customer related factors, wherein based on a machine learningalgorithm, a machine learning model is trained to perform the clusteringand the clustering is performed by the trained machine learning model;determining influencing factors associated with each of the subgroups;based on the influencing factors, generating a price elasticity curvefor each of the subgroups; based on the price elasticity curve andcurrent network traffic, determining cargo transportation priceassociated with each of the subgroups, wherein the influencing factorscomprise at least rate, chargeable weight (CWT), density, weight,volume, leadtime, number of pieces, freighter route, number of stops,customer industry, product code, special handling flag, seasonalityindices indicating CWT demand relative to average CWT, revenue demandrelative to average revenue and orders demand relative to averageorders, shipper capacity, average price quoted to customer in market,average price quoted to customer overall, customer percent (%) revenuethrough spot market, and customer % CWT through spot market; anddynamically balancing load in air traffic network in real-time viaproviding the cargo transportation price.
 10. The computer readablestorage medium of claim 9, wherein the cargo transportation price isdetermined for each of the subgroups simultaneously.
 11. (canceled) 12.The computer readable storage medium of claim 9, wherein the influencingfactors are correlated.
 13. The computer readable storage medium ofclaim 9, wherein the determining influencing factors comprises executinga LASSO technique.
 14. The computer readable storage medium of claim 9,wherein the determining cargo transportation price comprises executingan optimization function that maximizes revenue and a probability that aquoted price will be accepted.
 15. The computer readable storage mediumof claim 9, further comprising visually displaying the subgroups in agraphical user interface.
 16. The computer readable storage medium ofclaim 9, further comprising iterating the determining of the influencingfactors, generating of the price elasticity curve, and the determiningof the cargo transportation price.
 17. A system of training a machine toautomate spot pricing of logistics services comprising: at least onehardware processor; a display device coupled to the hardware processor;the hardware processor operable to receive a plurality oforiginal-destination routes in a network for transporting cargo; thehardware processor further operable to learn to train itself to clusterthe plurality of original-destination routes into a plurality ofclusters based on similarities of the original-destination routes; thehardware processor further operable to learn to train itself to clusterthe plurality of clusters into a plurality of subgroups based oncustomer related factors; the hardware processor further operable todetermine influencing factors associated with each of the subgroups, andbased on the influencing factors, generate a price elasticity curve foreach of the subgroups; the hardware processor further operable to, basedon the price elasticity curve and current network traffic, determinecargo transportation price associated with each of the subgroups; thehardware processor further operable to visually display the subgroups ina graphical user interface on the display device, wherein theinfluencing factors comprise at least rate, chargeable weight (CWT),density, weight, volume, leadtime, number of pieces, freighter route,number of stops, customer industry, product code, special handling flag,seasonality indices indicating CWT demand relative to average CWT,revenue demand relative to average revenue and orders demand relative toaverage orders, shipper capacity, average price quoted to customer inmarket, average price quoted to customer overall, customer percent (%)revenue through spot market, and customer % CWT through spot market; andthe hardware processor further operable to control dynamically balancingof load in air traffic network in real-time via providing the cargotransportation price.
 18. The system of claim 17, wherein the cargotransportation price is determined for each of the subgroupssimultaneously.
 19. (canceled)
 20. The system of claim 17, wherein thehardware processor is operable to execute an optimization function thatmaximizes revenue and a probability that a quoted price will beaccepted. 21.-23. (canceled)
 24. The method of claim 1, wherein theclustering is performed by training a decision tree model and aregression model, and merging the decision tree model and the regressionmodel to create interplay between segmentation and regression.